{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Colaboratory 简介",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/2033329616/tensorflow_project/blob/master/google/Colaboratory_%E7%AE%80%E4%BB%8B.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "9J7p406abzgl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " ## 欢迎使用 Colaboratory！\n",
        "\n",
        "Colaboratory 是一种数据分析工具，可将文字、代码和代码输出内容合并到一个协作文档中。"
      ]
    },
    {
      "metadata": {
        "id": "na0VvPXmYKp1",
        "colab_type": "code",
        "colab": {
          "height": 35
        },
        "outputId": "892d12d9-8635-41ea-9753-c8daa23340ff"
      },
      "cell_type": "code",
      "source": [
        "print('Hello, Colaboratory!')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello, Colaboratory!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S9GW-n-oYWIj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " 借助 Colaboratory，您只需点击一下鼠标，即可在浏览器中执行 TensorFlow 代码。下面的示例展示了两个矩阵相加的情况。\n",
        "\n",
        "$\\begin{bmatrix}\n",
        "  1. & 1. & 1. \\\\\n",
        "  1. & 1. & 1. \\\\\n",
        "\\end{bmatrix} +\n",
        "\\begin{bmatrix}\n",
        "  1. & 2. & 3. \\\\\n",
        "  4. & 5. & 6. \\\\\n",
        "\\end{bmatrix} =\n",
        "\\begin{bmatrix}\n",
        "  2. & 3. & 4. \\\\\n",
        "  5. & 6. & 7. \\\\\n",
        "\\end{bmatrix}$"
      ]
    },
    {
      "metadata": {
        "id": "mwdQ1INEZKkb",
        "colab_type": "code",
        "colab": {
          "height": 54
        },
        "outputId": "5b0e1dcb-e4e3-4c58-84ac-1b67c736cbbc"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "with tf.Session():\n",
        "  input1 = tf.constant(1.0, shape=[2, 3])\n",
        "  input2 = tf.constant(np.reshape(np.arange(1.0, 7.0, dtype=np.float32), (2, 3)))\n",
        "  output = tf.add(input1, input2)\n",
        "  result = output.eval()\n",
        "\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.,  3.,  4.],\n",
              "       [ 5.,  6.,  7.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "metadata": {
        "id": "rYs5mx2JZkmy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " Colaboratory 包含很多已被广泛使用的库（例如 [matplotlib](https://matplotlib.org/)），因而能够简化数据的可视化过程。"
      ]
    },
    {
      "metadata": {
        "id": "xqrc5C-IaA5J",
        "colab_type": "code",
        "colab": {
          "height": 276
        },
        "outputId": "0be85c6a-f98e-4756-bf6a-d2e88e8b156f"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = np.arange(20)\n",
        "y = [x_i + np.random.randn(1) for x_i in x]\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "plt.plot(x, y, 'o', np.arange(20), a*np.arange(20)+b, '-');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LNfXfQlltObpHjFAHRaNYV4jMqHSxyyIfwnAn8iKjtlEcufEBzrV/DgkkWJf8M2xOewqB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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcfe11fa650>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-Rh3-Vt9Nev9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " Colaboratory 可与 Google Cloud BigQuery 结合使用。\n",
        "\n",
        "[BigQuery 记事本范例](/notebook#fileId=/v2/external/notebooks/bigquery.ipynb)。"
      ]
    }
  ]
}